Overview

Dataset statistics

Number of variables18
Number of observations9798
Missing cells1152
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory144.0 B

Variable types

Categorical9
Boolean1
Numeric8

Alerts

key_studentInfo has a high cardinality: 9798 distinct valuesHigh cardinality
Moyenne de score has a high cardinality: 2156 distinct valuesHigh cardinality
Nombre de date_submitted is highly overall correlated with Nombre de date and 2 other fieldsHigh correlation
Nombre de date is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
sum_click is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
Nombre de activity_type is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
disability is highly imbalanced (54.2%)Imbalance
imd_band has 399 (4.1%) missing valuesMissing
Moyenne pondérée de score has 711 (7.3%) missing valuesMissing
key_studentInfo is uniformly distributedUniform
key_studentInfo has unique valuesUnique
num_of_prev_attempts has 8367 (85.4%) zerosZeros

Reproduction

Analysis started2023-03-21 10:23:50.907648
Analysis finished2023-03-21 10:23:58.306639
Duration7.4 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

key_studentInfo
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct9798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
1006742FFF2014B
 
1
579537BBB2014B
 
1
579081FFF2014B
 
1
579134DDD2014B
 
1
579255BBB2014B
 
1
Other values (9793)
9793 

Length

Max length15
Median length14
Mean length14.091039
Min length13

Characters and Unicode

Total characters138064
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9798 ?
Unique (%)100.0%

Sample

1st row1006742FFF2014B
2nd row1008675BBB2013B
3rd row101306FFF2013B
4th row101634FFF2014B
5th row1017773FFF2014B

Common Values

ValueCountFrequency (%)
1006742FFF2014B 1
 
< 0.1%
579537BBB2014B 1
 
< 0.1%
579081FFF2014B 1
 
< 0.1%
579134DDD2014B 1
 
< 0.1%
579255BBB2014B 1
 
< 0.1%
579340CCC2014B 1
 
< 0.1%
579343CCC2014B 1
 
< 0.1%
579372CCC2014B 1
 
< 0.1%
579376FFF2014B 1
 
< 0.1%
579550CCC2014B 1
 
< 0.1%
Other values (9788) 9788
99.9%

Length

2023-03-21T11:23:58.366699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1006742fff2014b 1
 
< 0.1%
1031884ddd2014b 1
 
< 0.1%
108834ccc2014b 1
 
< 0.1%
1049036ddd2014b 1
 
< 0.1%
101306fff2013b 1
 
< 0.1%
101634fff2014b 1
 
< 0.1%
1017773fff2014b 1
 
< 0.1%
1018685ddd2013b 1
 
< 0.1%
1026777fff2013b 1
 
< 0.1%
1033968ddd2014b 1
 
< 0.1%
Other values (9788) 9788
99.9%

Most occurring characters

ValueCountFrequency (%)
B 17520
12.7%
2 16552
12.0%
1 15516
11.2%
0 14603
10.6%
4 12327
8.9%
3 10121
7.3%
5 8385
 
6.1%
F 7731
 
5.6%
6 7389
 
5.4%
D 6021
 
4.4%
Other values (6) 21899
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98872
71.6%
Uppercase Letter 39192
 
28.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16552
16.7%
1 15516
15.7%
0 14603
14.8%
4 12327
12.5%
3 10121
10.2%
5 8385
8.5%
6 7389
7.5%
9 4694
 
4.7%
7 4659
 
4.7%
8 4626
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 17520
44.7%
F 7731
19.7%
D 6021
 
15.4%
C 4245
 
10.8%
G 2067
 
5.3%
E 1608
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 98872
71.6%
Latin 39192
 
28.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16552
16.7%
1 15516
15.7%
0 14603
14.8%
4 12327
12.5%
3 10121
10.2%
5 8385
8.5%
6 7389
7.5%
9 4694
 
4.7%
7 4659
 
4.7%
8 4626
 
4.7%
Latin
ValueCountFrequency (%)
B 17520
44.7%
F 7731
19.7%
D 6021
 
15.4%
C 4245
 
10.8%
G 2067
 
5.3%
E 1608
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 17520
12.7%
2 16552
12.0%
1 15516
11.2%
0 14603
10.6%
4 12327
8.9%
3 10121
7.3%
5 8385
 
6.1%
F 7731
 
5.6%
6 7389
 
5.4%
D 6021
 
4.4%
Other values (6) 21899
15.9%

final_result
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
Pass
4340 
Fail
2469 
Withdrawn
1878 
Distinction
1111 

Length

Max length11
Median length4
Mean length5.7520923
Min length4

Characters and Unicode

Total characters56359
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFail
2nd rowPass
3rd rowFail
4th rowFail
5th rowFail

Common Values

ValueCountFrequency (%)
Pass 4340
44.3%
Fail 2469
25.2%
Withdrawn 1878
19.2%
Distinction 1111
 
11.3%

Length

2023-03-21T11:23:58.465551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:58.564011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
pass 4340
44.3%
fail 2469
25.2%
withdrawn 1878
19.2%
distinction 1111
 
11.3%

Most occurring characters

ValueCountFrequency (%)
s 9791
17.4%
a 8687
15.4%
i 7680
13.6%
P 4340
7.7%
t 4100
7.3%
n 4100
7.3%
F 2469
 
4.4%
l 2469
 
4.4%
W 1878
 
3.3%
h 1878
 
3.3%
Other values (6) 8967
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46561
82.6%
Uppercase Letter 9798
 
17.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9791
21.0%
a 8687
18.7%
i 7680
16.5%
t 4100
8.8%
n 4100
8.8%
l 2469
 
5.3%
h 1878
 
4.0%
d 1878
 
4.0%
r 1878
 
4.0%
w 1878
 
4.0%
Other values (2) 2222
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 4340
44.3%
F 2469
25.2%
W 1878
19.2%
D 1111
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 56359
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9791
17.4%
a 8687
15.4%
i 7680
13.6%
P 4340
7.7%
t 4100
7.3%
n 4100
7.3%
F 2469
 
4.4%
l 2469
 
4.4%
W 1878
 
3.3%
h 1878
 
3.3%
Other values (6) 8967
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9791
17.4%
a 8687
15.4%
i 7680
13.6%
P 4340
7.7%
t 4100
7.3%
n 4100
7.3%
F 2469
 
4.4%
l 2469
 
4.4%
W 1878
 
3.3%
h 1878
 
3.3%
Other values (6) 8967
15.9%

disability
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
False
8851 
True
947 
ValueCountFrequency (%)
False 8851
90.3%
True 947
 
9.7%
2023-03-21T11:23:58.648039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
241
4788 
240
3805 
234
1205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29394
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row241
2nd row240
3rd row240
4th row241
5th row241

Common Values

ValueCountFrequency (%)
241 4788
48.9%
240 3805
38.8%
234 1205
 
12.3%

Length

2023-03-21T11:23:58.716039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:58.796038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
241 4788
48.9%
240 3805
38.8%
234 1205
 
12.3%

Most occurring characters

ValueCountFrequency (%)
2 9798
33.3%
4 9798
33.3%
1 4788
16.3%
0 3805
 
12.9%
3 1205
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9798
33.3%
4 9798
33.3%
1 4788
16.3%
0 3805
 
12.9%
3 1205
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 29394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9798
33.3%
4 9798
33.3%
1 4788
16.3%
0 3805
 
12.9%
3 1205
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9798
33.3%
4 9798
33.3%
1 4788
16.3%
0 3805
 
12.9%
3 1205
 
4.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
M
5302 
F
4496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 5302
54.1%
F 4496
45.9%

Length

2023-03-21T11:23:58.868041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:58.944037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
m 5302
54.1%
f 4496
45.9%

Most occurring characters

ValueCountFrequency (%)
M 5302
54.1%
F 4496
45.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9798
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5302
54.1%
F 4496
45.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9798
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5302
54.1%
F 4496
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5302
54.1%
F 4496
45.9%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
A Level or Equivalent
4346 
Lower Than A Level
3776 
HE Qualification
1453 
Post Graduate Qualification
 
118
No Formal quals
 
105

Length

Max length27
Median length21
Mean length19.110329
Min length15

Characters and Unicode

Total characters187243
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHE Qualification
2nd rowLower Than A Level
3rd rowA Level or Equivalent
4th rowNo Formal quals
5th rowLower Than A Level

Common Values

ValueCountFrequency (%)
A Level or Equivalent 4346
44.4%
Lower Than A Level 3776
38.5%
HE Qualification 1453
 
14.8%
Post Graduate Qualification 118
 
1.2%
No Formal quals 105
 
1.1%

Length

2023-03-21T11:23:59.012040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:59.096209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
a 8122
22.5%
level 8122
22.5%
or 4346
12.1%
equivalent 4346
12.1%
lower 3776
10.5%
than 3776
10.5%
qualification 1571
 
4.4%
he 1453
 
4.0%
post 118
 
0.3%
graduate 118
 
0.3%
Other values (3) 315
 
0.9%

Most occurring characters

ValueCountFrequency (%)
26265
14.0%
e 24484
13.1%
l 14249
 
7.6%
v 12468
 
6.7%
L 11898
 
6.4%
a 11710
 
6.3%
o 10021
 
5.4%
n 9693
 
5.2%
i 9059
 
4.8%
r 8345
 
4.5%
Other values (19) 49051
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127913
68.3%
Uppercase Letter 33065
 
17.7%
Space Separator 26265
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24484
19.1%
l 14249
11.1%
v 12468
9.7%
a 11710
9.2%
o 10021
7.8%
n 9693
 
7.6%
i 9059
 
7.1%
r 8345
 
6.5%
t 6153
 
4.8%
u 6140
 
4.8%
Other values (8) 15591
12.2%
Uppercase Letter
ValueCountFrequency (%)
L 11898
36.0%
A 8122
24.6%
E 5799
17.5%
T 3776
 
11.4%
Q 1571
 
4.8%
H 1453
 
4.4%
P 118
 
0.4%
G 118
 
0.4%
N 105
 
0.3%
F 105
 
0.3%
Space Separator
ValueCountFrequency (%)
26265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160978
86.0%
Common 26265
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24484
15.2%
l 14249
 
8.9%
v 12468
 
7.7%
L 11898
 
7.4%
a 11710
 
7.3%
o 10021
 
6.2%
n 9693
 
6.0%
i 9059
 
5.6%
r 8345
 
5.2%
A 8122
 
5.0%
Other values (18) 40929
25.4%
Common
ValueCountFrequency (%)
26265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26265
14.0%
e 24484
13.1%
l 14249
 
7.6%
v 12468
 
6.7%
L 11898
 
6.4%
a 11710
 
6.3%
o 10021
 
5.4%
n 9693
 
5.2%
i 9059
 
4.8%
r 8345
 
4.5%
Other values (19) 49051
26.2%

imd_band
Categorical

Distinct10
Distinct (%)0.1%
Missing399
Missing (%)4.1%
Memory size76.7 KiB
20-30%
1087 
30-40%
1055 
10-20
988 
0-10%
974 
40-50%
946 
Other values (5)
4349 

Length

Max length7
Median length6
Mean length5.8760506
Min length5

Characters and Unicode

Total characters55229
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80-90%
2nd row90-100%
3rd row20-30%
4th row0-10%
5th row70-80%

Common Values

ValueCountFrequency (%)
20-30% 1087
11.1%
30-40% 1055
10.8%
10-20 988
10.1%
0-10% 974
9.9%
40-50% 946
9.7%
50-60% 941
9.6%
60-70% 885
9.0%
70-80% 881
9.0%
80-90% 845
8.6%
90-100% 797
8.1%
(Missing) 399
 
4.1%

Length

2023-03-21T11:23:59.196501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:59.312443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
20-30 1087
11.6%
30-40 1055
11.2%
10-20 988
10.5%
0-10 974
10.4%
40-50 946
10.1%
50-60 941
10.0%
60-70 885
9.4%
70-80 881
9.4%
80-90 845
9.0%
90-100 797
8.5%

Most occurring characters

ValueCountFrequency (%)
0 19595
35.5%
- 9399
17.0%
% 8411
15.2%
1 2759
 
5.0%
3 2142
 
3.9%
2 2075
 
3.8%
4 2001
 
3.6%
5 1887
 
3.4%
6 1826
 
3.3%
7 1766
 
3.2%
Other values (2) 3368
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37419
67.8%
Dash Punctuation 9399
 
17.0%
Other Punctuation 8411
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19595
52.4%
1 2759
 
7.4%
3 2142
 
5.7%
2 2075
 
5.5%
4 2001
 
5.3%
5 1887
 
5.0%
6 1826
 
4.9%
7 1766
 
4.7%
8 1726
 
4.6%
9 1642
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 9399
100.0%
Other Punctuation
ValueCountFrequency (%)
% 8411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19595
35.5%
- 9399
17.0%
% 8411
15.2%
1 2759
 
5.0%
3 2142
 
3.9%
2 2075
 
3.8%
4 2001
 
3.6%
5 1887
 
3.4%
6 1826
 
3.3%
7 1766
 
3.2%
Other values (2) 3368
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19595
35.5%
- 9399
17.0%
% 8411
15.2%
1 2759
 
5.0%
3 2142
 
3.9%
2 2075
 
3.8%
4 2001
 
3.6%
5 1887
 
3.4%
6 1826
 
3.3%
7 1766
 
3.2%
Other values (2) 3368
 
6.1%

num_of_prev_attempts
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18595632
Minimum0
Maximum6
Zeros8367
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:23:59.412735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5068871
Coefficient of variation (CV)2.7258396
Kurtosis17.089851
Mean0.18595632
Median Absolute Deviation (MAD)0
Skewness3.532839
Sum1822
Variance0.25693453
MonotonicityNot monotonic
2023-03-21T11:23:59.475220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 8367
85.4%
1 1129
 
11.5%
2 240
 
2.4%
3 43
 
0.4%
4 13
 
0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 8367
85.4%
1 1129
 
11.5%
2 240
 
2.4%
3 43
 
0.4%
4 13
 
0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 4
 
< 0.1%
4 13
 
0.1%
3 43
 
0.4%
2 240
 
2.4%
1 1129
 
11.5%
0 8367
85.4%

region
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
East Anglian Region
1027 
London Region
994 
South Region
981 
Scotland
929 
North Western Region
838 
Other values (8)
5029 

Length

Max length20
Median length17
Mean length14.675852
Min length5

Characters and Unicode

Total characters143794
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScotland
2nd rowEast Anglian Region
3rd rowLondon Region
4th rowYorkshire Region
5th rowWest Midlands Region

Common Values

ValueCountFrequency (%)
East Anglian Region 1027
10.5%
London Region 994
10.1%
South Region 981
10.0%
Scotland 929
9.5%
North Western Region 838
8.6%
West Midlands Region 775
7.9%
South West Region 775
7.9%
East Midlands Region 705
7.2%
Wales 648
6.6%
South East Region 626
6.4%
Other values (3) 1500
15.3%

Length

2023-03-21T11:23:59.559861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region 7883
35.1%
south 2382
 
10.6%
east 2358
 
10.5%
west 1550
 
6.9%
midlands 1480
 
6.6%
north 1393
 
6.2%
anglian 1027
 
4.6%
london 994
 
4.4%
scotland 929
 
4.1%
western 838
 
3.7%
Other values (3) 1593
 
7.1%

Most occurring characters

ValueCountFrequency (%)
n 15510
10.8%
o 15182
10.6%
e 12702
 
8.8%
12629
 
8.8%
i 10997
 
7.6%
t 9450
 
6.6%
g 8910
 
6.2%
R 7883
 
5.5%
s 7481
 
5.2%
a 6780
 
4.7%
Other values (16) 36270
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108738
75.6%
Uppercase Letter 22427
 
15.6%
Space Separator 12629
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 15510
14.3%
o 15182
14.0%
e 12702
11.7%
i 10997
10.1%
t 9450
8.7%
g 8910
8.2%
s 7481
6.9%
a 6780
6.2%
d 5221
 
4.8%
l 4422
 
4.1%
Other values (5) 12083
11.1%
Uppercase Letter
ValueCountFrequency (%)
R 7883
35.1%
S 3311
14.8%
W 3036
 
13.5%
E 2358
 
10.5%
M 1480
 
6.6%
N 1393
 
6.2%
A 1027
 
4.6%
L 994
 
4.4%
Y 607
 
2.7%
I 338
 
1.5%
Space Separator
ValueCountFrequency (%)
12629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131165
91.2%
Common 12629
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 15510
11.8%
o 15182
11.6%
e 12702
9.7%
i 10997
 
8.4%
t 9450
 
7.2%
g 8910
 
6.8%
R 7883
 
6.0%
s 7481
 
5.7%
a 6780
 
5.2%
d 5221
 
4.0%
Other values (15) 31049
23.7%
Common
ValueCountFrequency (%)
12629
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 15510
10.8%
o 15182
10.6%
e 12702
 
8.8%
12629
 
8.8%
i 10997
 
7.6%
t 9450
 
6.6%
g 8910
 
6.2%
R 7883
 
5.5%
s 7481
 
5.2%
a 6780
 
4.7%
Other values (16) 36270
25.2%

studied_credits
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.252194
Minimum30
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:23:59.675779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q160
median60
Q390
95-th percentile150
Maximum630
Range600
Interquartile range (IQR)30

Descriptive statistics

Standard deviation39.823593
Coefficient of variation (CV)0.50891343
Kurtosis7.9170733
Mean78.252194
Median Absolute Deviation (MAD)0
Skewness1.9386468
Sum766715
Variance1585.9185
MonotonicityNot monotonic
2023-03-21T11:23:59.760433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
60 5369
54.8%
120 1717
 
17.5%
30 1056
 
10.8%
90 856
 
8.7%
180 243
 
2.5%
150 221
 
2.3%
240 55
 
0.6%
210 48
 
0.5%
75 47
 
0.5%
70 47
 
0.5%
Other values (31) 139
 
1.4%
ValueCountFrequency (%)
30 1056
 
10.8%
40 9
 
0.1%
45 6
 
0.1%
60 5369
54.8%
70 47
 
0.5%
75 47
 
0.5%
80 14
 
0.1%
90 856
 
8.7%
100 10
 
0.1%
105 4
 
< 0.1%
ValueCountFrequency (%)
630 1
 
< 0.1%
360 2
 
< 0.1%
355 1
 
< 0.1%
330 1
 
< 0.1%
325 1
 
< 0.1%
310 2
 
< 0.1%
300 9
0.1%
280 1
 
< 0.1%
270 14
0.1%
250 2
 
< 0.1%

age_band
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.7 KiB
0-35
6865 
35-55
2886 
55<=
 
47

Length

Max length5
Median length4
Mean length4.2945499
Min length4

Characters and Unicode

Total characters42078
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55<=
2nd row35-55
3rd row35-55
4th row0-35
5th row35-55

Common Values

ValueCountFrequency (%)
0-35 6865
70.1%
35-55 2886
29.5%
55<= 47
 
0.5%

Length

2023-03-21T11:23:59.860720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:23:59.945386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0-35 6865
70.1%
35-55 2886
29.5%
55 47
 
0.5%

Most occurring characters

ValueCountFrequency (%)
5 15617
37.1%
- 9751
23.2%
3 9751
23.2%
0 6865
16.3%
< 47
 
0.1%
= 47
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32233
76.6%
Dash Punctuation 9751
 
23.2%
Math Symbol 94
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 15617
48.5%
3 9751
30.3%
0 6865
21.3%
Math Symbol
ValueCountFrequency (%)
< 47
50.0%
= 47
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 9751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 15617
37.1%
- 9751
23.2%
3 9751
23.2%
0 6865
16.3%
< 47
 
0.1%
= 47
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 15617
37.1%
- 9751
23.2%
3 9751
23.2%
0 6865
16.3%
< 47
 
0.1%
= 47
 
0.1%

Nombre de date_submitted
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2307614
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:23:59.998783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile12
Maximum13
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9823696
Coefficient of variation (CV)0.55075385
Kurtosis-1.4268671
Mean7.2307614
Median Absolute Deviation (MAD)4
Skewness-0.16886067
Sum70847
Variance15.859268
MonotonicityNot monotonic
2023-03-21T11:24:00.076928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12 1578
16.1%
11 1234
12.6%
1 1000
10.2%
4 933
9.5%
2 698
7.1%
8 697
7.1%
6 685
7.0%
3 664
6.8%
9 624
 
6.4%
10 576
 
5.9%
Other values (3) 1109
11.3%
ValueCountFrequency (%)
1 1000
10.2%
2 698
7.1%
3 664
6.8%
4 933
9.5%
5 426
4.3%
6 685
7.0%
7 309
 
3.2%
8 697
7.1%
9 624
6.4%
10 576
5.9%
ValueCountFrequency (%)
13 374
 
3.8%
12 1578
16.1%
11 1234
12.6%
10 576
 
5.9%
9 624
 
6.4%
8 697
7.1%
7 309
 
3.2%
6 685
7.0%
5 426
 
4.3%
4 933
9.5%

Moyenne de score
Categorical

Distinct2156
Distinct (%)22.0%
Missing11
Missing (%)0.1%
Memory size76.7 KiB
80,00
 
87
60,00
 
72
70,00
 
69
76,00
 
69
74,00
 
68
Other values (2151)
9422 

Length

Max length6
Median length5
Mean length4.996526
Min length4

Characters and Unicode

Total characters48901
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)8.0%

Sample

1st row78,50
2nd row76,91
3rd row49,00
4th row84,00
5th row80,00

Common Values

ValueCountFrequency (%)
80,00 87
 
0.9%
60,00 72
 
0.7%
70,00 69
 
0.7%
76,00 69
 
0.7%
74,00 68
 
0.7%
84,00 67
 
0.7%
75,00 67
 
0.7%
77,00 61
 
0.6%
65,00 61
 
0.6%
82,00 60
 
0.6%
Other values (2146) 9106
92.9%

Length

2023-03-21T11:24:00.177207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80,00 87
 
0.9%
60,00 72
 
0.7%
70,00 69
 
0.7%
76,00 69
 
0.7%
74,00 68
 
0.7%
84,00 67
 
0.7%
75,00 67
 
0.7%
77,00 61
 
0.6%
65,00 61
 
0.6%
82,00 60
 
0.6%
Other values (2146) 9106
93.0%

Most occurring characters

ValueCountFrequency (%)
, 9787
20.0%
0 8467
17.3%
8 5335
10.9%
7 5282
10.8%
5 4236
8.7%
6 3704
 
7.6%
3 3180
 
6.5%
2 2442
 
5.0%
9 2418
 
4.9%
4 2146
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39114
80.0%
Other Punctuation 9787
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8467
21.6%
8 5335
13.6%
7 5282
13.5%
5 4236
10.8%
6 3704
9.5%
3 3180
 
8.1%
2 2442
 
6.2%
9 2418
 
6.2%
4 2146
 
5.5%
1 1904
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 9787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48901
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 9787
20.0%
0 8467
17.3%
8 5335
10.9%
7 5282
10.8%
5 4236
8.7%
6 3704
 
7.6%
3 3180
 
6.5%
2 2442
 
5.0%
9 2418
 
4.9%
4 2146
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 9787
20.0%
0 8467
17.3%
8 5335
10.9%
7 5282
10.8%
5 4236
8.7%
6 3704
 
7.6%
3 3180
 
6.5%
2 2442
 
5.0%
9 2418
 
4.9%
4 2146
 
4.4%
Distinct6986
Distinct (%)76.9%
Missing711
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean68.671919
Minimum0
Maximum100
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:24:00.442920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.799496
Q158.795158
median72
Q382.076693
95-th percentile92.298695
Maximum100
Range100
Interquartile range (IQR)23.281536

Descriptive statistics

Standard deviation18.227549
Coefficient of variation (CV)0.26542944
Kurtosis0.8674386
Mean68.671919
Median Absolute Deviation (MAD)11.4
Skewness-0.94763468
Sum624021.73
Variance332.24356
MonotonicityNot monotonic
2023-03-21T11:24:00.544713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 51
 
0.5%
50 46
 
0.5%
60 45
 
0.5%
80 45
 
0.5%
70 44
 
0.4%
30 40
 
0.4%
64 36
 
0.4%
20 35
 
0.4%
76 33
 
0.3%
82 32
 
0.3%
Other values (6976) 8680
88.6%
(Missing) 711
 
7.3%
ValueCountFrequency (%)
0 29
0.3%
1.421375085 1
 
< 0.1%
1.581632653 1
 
< 0.1%
1.831423895 1
 
< 0.1%
2.284379172 1
 
< 0.1%
2.516688919 2
 
< 0.1%
2.632843792 1
 
< 0.1%
3 1
 
< 0.1%
3.329773031 1
 
< 0.1%
3.368491322 1
 
< 0.1%
ValueCountFrequency (%)
100 12
0.1%
99.8 1
 
< 0.1%
99.63934426 1
 
< 0.1%
99.57874208 1
 
< 0.1%
99.23025584 1
 
< 0.1%
99.16 1
 
< 0.1%
99.01324806 1
 
< 0.1%
99 1
 
< 0.1%
98.94 1
 
< 0.1%
98.90726359 1
 
< 0.1%

date_registration
Real number (ℝ)

Distinct294
Distinct (%)3.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-62.594059
Minimum-311
Maximum167
Zeros0
Zeros (%)0.0%
Negative9732
Negative (%)99.3%
Memory size76.7 KiB
2023-03-21T11:24:00.656596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-311
5-th percentile-155
Q1-87
median-50
Q3-25
95-th percentile-15
Maximum167
Range478
Interquartile range (IQR)62

Descriptive statistics

Standard deviation49.394774
Coefficient of variation (CV)-0.78912878
Kurtosis3.1205665
Mean-62.594059
Median Absolute Deviation (MAD)27
Skewness-1.5338931
Sum-613234
Variance2439.8437
MonotonicityNot monotonic
2023-03-21T11:24:00.756886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22 503
 
5.1%
-24 400
 
4.1%
-29 391
 
4.0%
-25 333
 
3.4%
-23 282
 
2.9%
-17 228
 
2.3%
-28 191
 
1.9%
-36 184
 
1.9%
-15 180
 
1.8%
-34 156
 
1.6%
Other values (284) 6949
70.9%
ValueCountFrequency (%)
-311 1
 
< 0.1%
-310 2
< 0.1%
-305 2
< 0.1%
-304 3
< 0.1%
-303 1
 
< 0.1%
-302 1
 
< 0.1%
-298 1
 
< 0.1%
-297 2
< 0.1%
-295 1
 
< 0.1%
-290 1
 
< 0.1%
ValueCountFrequency (%)
167 1
< 0.1%
124 1
< 0.1%
82 1
< 0.1%
81 1
< 0.1%
69 1
< 0.1%
59 1
< 0.1%
49 1
< 0.1%
40 1
< 0.1%
28 2
< 0.1%
27 1
< 0.1%

Nombre de date
Real number (ℝ)

Distinct1496
Distinct (%)15.3%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean409.10646
Minimum1
Maximum3078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:24:00.863702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1133
median287
Q3563
95-th percentile1198
Maximum3078
Range3077
Interquartile range (IQR)430

Descriptive statistics

Standard deviation381.66477
Coefficient of variation (CV)0.93292287
Kurtosis3.8977803
Mean409.10646
Median Absolute Deviation (MAD)184
Skewness1.7491851
Sum4004334
Variance145668
MonotonicityNot monotonic
2023-03-21T11:24:00.963998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 34
 
0.3%
66 33
 
0.3%
103 32
 
0.3%
120 31
 
0.3%
140 31
 
0.3%
68 31
 
0.3%
63 31
 
0.3%
94 31
 
0.3%
112 29
 
0.3%
114 29
 
0.3%
Other values (1486) 9476
96.7%
ValueCountFrequency (%)
1 7
0.1%
2 4
 
< 0.1%
3 9
0.1%
4 5
 
0.1%
5 7
0.1%
6 13
0.1%
7 7
0.1%
8 11
0.1%
9 8
0.1%
10 7
0.1%
ValueCountFrequency (%)
3078 1
< 0.1%
2942 1
< 0.1%
2833 1
< 0.1%
2683 1
< 0.1%
2625 1
< 0.1%
2620 1
< 0.1%
2565 1
< 0.1%
2557 1
< 0.1%
2519 1
< 0.1%
2503 1
< 0.1%

sum_click
Real number (ℝ)

Distinct3662
Distinct (%)37.4%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1505.7898
Minimum1
Maximum21123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:24:01.079942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102
Q1372
median863
Q31986
95-th percentile4989.2
Maximum21123
Range21122
Interquartile range (IQR)1614

Descriptive statistics

Standard deviation1760.7704
Coefficient of variation (CV)1.1693334
Kurtosis10.689766
Mean1505.7898
Median Absolute Deviation (MAD)608
Skewness2.6332453
Sum14738671
Variance3100312.3
MonotonicityNot monotonic
2023-03-21T11:24:01.180232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 16
 
0.2%
75 15
 
0.2%
151 15
 
0.2%
328 14
 
0.1%
90 14
 
0.1%
248 14
 
0.1%
653 13
 
0.1%
192 13
 
0.1%
322 13
 
0.1%
188 13
 
0.1%
Other values (3652) 9648
98.5%
ValueCountFrequency (%)
1 6
0.1%
2 1
 
< 0.1%
3 3
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 3
< 0.1%
7 5
0.1%
8 1
 
< 0.1%
9 6
0.1%
10 4
< 0.1%
ValueCountFrequency (%)
21123 1
< 0.1%
18039 1
< 0.1%
16372 1
< 0.1%
15858 1
< 0.1%
15300 1
< 0.1%
15205 1
< 0.1%
14841 1
< 0.1%
14391 1
< 0.1%
14172 1
< 0.1%
13874 1
< 0.1%

Nombre de activity_type
Real number (ℝ)

Distinct307
Distinct (%)3.1%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean76.155088
Minimum1
Maximum413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.7 KiB
2023-03-21T11:24:01.280523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q133
median57
Q3105
95-th percentile197
Maximum413
Range412
Interquartile range (IQR)72

Descriptive statistics

Standard deviation58.179534
Coefficient of variation (CV)0.76396122
Kurtosis1.3940078
Mean76.155088
Median Absolute Deviation (MAD)30
Skewness1.282761
Sum745406
Variance3384.8581
MonotonicityNot monotonic
2023-03-21T11:24:01.380823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 129
 
1.3%
39 125
 
1.3%
28 123
 
1.3%
33 121
 
1.2%
36 119
 
1.2%
40 118
 
1.2%
30 115
 
1.2%
32 114
 
1.2%
37 114
 
1.2%
35 114
 
1.2%
Other values (297) 8596
87.7%
ValueCountFrequency (%)
1 9
 
0.1%
2 21
0.2%
3 21
0.2%
4 22
0.2%
5 21
0.2%
6 29
0.3%
7 28
0.3%
8 35
0.4%
9 33
0.3%
10 49
0.5%
ValueCountFrequency (%)
413 1
< 0.1%
403 1
< 0.1%
349 1
< 0.1%
344 1
< 0.1%
341 1
< 0.1%
334 1
< 0.1%
328 1
< 0.1%
324 2
< 0.1%
322 1
< 0.1%
320 2
< 0.1%

Interactions

2023-03-21T11:23:56.988175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:51.844571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.565086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.415972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.137095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.853836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.552053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.288203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.076171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:51.933141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.661059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.511526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.229065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.945831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.648079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.380204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.166704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.029143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.749086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.604422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.325066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.037832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.744051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.472202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.258700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.117140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.841086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.692430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.413064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.122300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.836051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.560182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.346671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.213398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.932477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.782756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.501093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.212977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.932079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.648180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.429737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.301104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.016710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.867378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.589804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.292976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.020702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.732172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.522977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.393069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.110350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.961167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.681829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.384976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.112731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.820172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:57.606976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:52.481076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:53.332004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.053166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:54.769832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:55.468724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.200701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:23:56.904171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-21T11:24:01.481116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
num_of_prev_attemptsstudied_creditsNombre de date_submittedMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_typefinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandregionage_band
num_of_prev_attempts1.0000.394-0.028-0.066-0.030-0.076-0.084-0.0440.0660.0680.0860.0280.0220.0100.0190.000
studied_credits0.3941.0000.052-0.089-0.1130.021-0.0020.0760.0360.0590.1220.0350.0000.0170.0240.028
Nombre de date_submitted-0.0280.0521.0000.2530.0120.6130.5790.5730.4160.0540.4260.3900.0730.0350.0500.061
Moyenne pondérée de score-0.066-0.0890.2531.0000.0220.3800.3750.2680.3770.0610.1480.1530.0820.0470.0290.042
date_registration-0.030-0.1130.0120.0221.000-0.023-0.018-0.0050.0450.0240.1200.0570.0220.0070.0260.017
Nombre de date-0.0760.0210.6130.380-0.0231.0000.9640.8900.2710.0480.1590.1610.0530.0320.0430.081
sum_click-0.084-0.0020.5790.375-0.0180.9641.0000.8430.2040.0380.1260.1630.0350.0000.0380.061
Nombre de activity_type-0.0440.0760.5730.268-0.0050.8900.8431.0000.2300.0300.2360.2510.0650.0280.0400.076
final_result0.0660.0360.4160.3770.0450.2710.2040.2301.0000.0660.0730.0420.1000.0800.0560.056
disability0.0680.0590.0540.0610.0240.0480.0380.0300.0661.0000.0000.0430.1040.0810.0650.025
module_presentation_length0.0860.1220.4260.1480.1200.1590.1260.2360.0730.0001.0000.3660.0470.0370.0860.033
gender0.0280.0350.3900.1530.0570.1610.1630.2510.0420.0430.3661.0000.0810.0900.0930.056
highest_education0.0220.0000.0730.0820.0220.0530.0350.0650.1000.1040.0470.0811.0000.0880.1420.148
imd_band0.0100.0170.0350.0470.0070.0320.0000.0280.0800.0810.0370.0900.0881.0000.1360.061
region0.0190.0240.0500.0290.0260.0430.0380.0400.0560.0650.0860.0930.1420.1361.0000.052
age_band0.0000.0280.0610.0420.0170.0810.0610.0760.0560.0250.0330.0560.1480.0610.0521.000

Missing values

2023-03-21T11:23:57.753442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-21T11:23:58.012725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-21T11:23:58.210638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

key_studentInfofinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandnum_of_prev_attemptsregionstudied_creditsage_bandNombre de date_submittedMoyenne de scoreMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_type
01006742FFF2014BFailN241MHE Qualification80-90%1Scotland12055<=278,5078.000000-78.0659.02421.0113.0
11008675BBB2013BPassN240FLower Than A Level90-100%0East Anglian Region6035-551176,9168.380000-48.0684.01913.067.0
2101306FFF2013BFailN240MA Level or Equivalent20-30%0London Region12035-55249,0028.829135-148.0289.0866.0101.0
3101634FFF2014BFailN241MNo Formal quals0-10%0Yorkshire Region900-35284,0084.000000-50.0102.0380.048.0
41017773FFF2014BFailN241MLower Than A Level70-80%0West Midlands Region6035-55180,0080.000000-25.071.0145.033.0
51018685DDD2013BFailN240MLower Than A Level20-30%0North Region6035-55241,5040.385779-34.0349.0754.089.0
61026777FFF2013BWithdrawnN240MLower Than A Level60-70%1South Region21035-55287,0086.000000-20.0114.0552.040.0
71031884DDD2014BDistinctionN241MA Level or Equivalent80-90%0East Midlands Region6035-55692,5089.721112-22.0488.01084.0168.0
81033968DDD2014BWithdrawnN241FNo Formal quals0-10%1Wales6035-55262,5073.801653-23.083.0140.045.0
91034674FFF2013BPassN240MA Level or Equivalent60-70%0South East Region6035-551288,9280.092736-48.01251.06543.0265.0
key_studentInfofinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandnum_of_prev_attemptsregionstudied_creditsage_bandNombre de date_submittedMoyenne de scoreMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_type
978898313BBB2013BPassN240FHE Qualification30-40%0Scotland6035-551180,7378.820000-22.069.0145.023.0
9789984619CCC2014BPassN241MHE Qualification90-100%0South West Region3035-55877,3878.440000-179.0983.03168.0127.0
9790988019FFF2013BWithdrawnY240FA Level or Equivalent20-30%0London Region6035-55388,3393.000000-22.0590.01627.0113.0
979199151DDD2014BFailN241MLower Than A Level40-50%0London Region1200-35146,0046.000000-44.072.0397.031.0
9792992544CCC2014BPassN241MPost Graduate Qualification80-90%0Scotland18035-55495,0096.000000-147.0165.0410.022.0
9793996047EEE2014BPassN241MA Level or Equivalent20-30%0North Western Region6035-55481,0082.320000-17.0591.02708.058.0
979499670FFF2014BFailN241FLower Than A Level0-10%0North Western Region900-35891,7588.395158-27.0467.02109.0107.0
979599799BBB2014BPassN234FA Level or Equivalent10-201East Midlands Region1200-351075,9076.414634-41.0246.0553.044.0
9796999174FFF2013BPassN240MHE Qualification70-80%1South East Region15055<=1285,5079.395158-58.02209.06307.0283.0
979799993FFF2013BPassN240FA Level or Equivalent0-10%0East Anglian Region1200-351176,1879.843307-94.0950.03922.0167.0